Overview

Dataset statistics

Number of variables33
Number of observations658
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory169.8 KiB
Average record size in memory264.2 B

Variable types

Categorical20
Numeric13

Alerts

ParticipantID has a high cardinality: 626 distinct valuesHigh cardinality
MSSS1 is highly overall correlated with MSSS2High correlation
MSSS2 is highly overall correlated with MSSS1 and 1 other fieldsHigh correlation
MSSS3 is highly overall correlated with MSSS4 and 1 other fieldsHigh correlation
MSSS4 is highly overall correlated with MSSS3 and 2 other fieldsHigh correlation
MSSS5 is highly overall correlated with MSSS2 and 1 other fieldsHigh correlation
MSSS6 is highly overall correlated with MSSS7 and 1 other fieldsHigh correlation
MSSS7 is highly overall correlated with MSSS6High correlation
MSSS8 is highly overall correlated with MSSS4High correlation
MSSS9 is highly overall correlated with MSSS6 and 1 other fieldsHigh correlation
MSSS10 is highly overall correlated with MSSS5High correlation
MSSS11 is highly overall correlated with MSSS3 and 1 other fieldsHigh correlation
MSSS12 is highly overall correlated with MSSS9High correlation
Tribe is highly overall correlated with School and 1 other fieldsHigh correlation
Gender is highly overall correlated with SchoolHigh correlation
School is highly overall correlated with Tribe and 2 other fieldsHigh correlation
School_Resources is highly overall correlated with Tribe and 1 other fieldsHigh correlation
ParticipantID is uniformly distributedUniform

Reproduction

Analysis started2023-01-12 12:06:50.857521
Analysis finished2023-01-12 12:07:52.387741
Duration1 minute and 1.53 second
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

ParticipantID
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct626
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
AHS_127
 
2
AHS_116
 
2
AHS_132
 
2
AHS_131
 
2
AHS_130
 
2
Other values (621)
648 

Length

Max length8
Median length7
Mean length6.9848024
Min length6

Characters and Unicode

Total characters4596
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)90.3%

Sample

1st rowSR_001
2nd rowSR_002
3rd rowSR_003
4th rowSR_004
5th rowSR_005

Common Values

ValueCountFrequency (%)
AHS_127 2
 
0.3%
AHS_116 2
 
0.3%
AHS_132 2
 
0.3%
AHS_131 2
 
0.3%
AHS_130 2
 
0.3%
AHS_129 2
 
0.3%
AHS_128 2
 
0.3%
AHS_126 2
 
0.3%
AHS_125 2
 
0.3%
AHS_124 2
 
0.3%
Other values (616) 638
97.0%

Length

2023-01-12T12:07:52.578221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ahs_127 2
 
0.3%
ahs_115 2
 
0.3%
ahs_116 2
 
0.3%
ahs_109 2
 
0.3%
ahs_113 2
 
0.3%
ahs_112 2
 
0.3%
ahs_111 2
 
0.3%
ahs_110 2
 
0.3%
ahs_108 2
 
0.3%
ahs_104 2
 
0.3%
Other values (616) 638
97.0%

Most occurring characters

ValueCountFrequency (%)
_ 658
14.3%
0 563
12.2%
1 382
 
8.3%
H 366
 
8.0%
S 359
 
7.8%
A 279
 
6.1%
L 212
 
4.6%
O 212
 
4.6%
Y 212
 
4.6%
2 161
 
3.5%
Other values (10) 1192
25.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1974
43.0%
Uppercase Letter 1964
42.7%
Connector Punctuation 658
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563
28.5%
1 382
19.4%
2 161
 
8.2%
3 139
 
7.0%
5 133
 
6.7%
4 127
 
6.4%
6 126
 
6.4%
7 126
 
6.4%
8 113
 
5.7%
9 104
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
H 366
18.6%
S 359
18.3%
A 279
14.2%
L 212
10.8%
O 212
10.8%
Y 212
10.8%
G 157
8.0%
E 87
 
4.4%
R 80
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_ 658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2632
57.3%
Latin 1964
42.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 658
25.0%
0 563
21.4%
1 382
14.5%
2 161
 
6.1%
3 139
 
5.3%
5 133
 
5.1%
4 127
 
4.8%
6 126
 
4.8%
7 126
 
4.8%
8 113
 
4.3%
Latin
ValueCountFrequency (%)
H 366
18.6%
S 359
18.3%
A 279
14.2%
L 212
10.8%
O 212
10.8%
Y 212
10.8%
G 157
8.0%
E 87
 
4.4%
R 80
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 658
14.3%
0 563
12.2%
1 382
 
8.3%
H 366
 
8.0%
S 359
 
7.8%
A 279
 
6.1%
L 212
 
4.6%
O 212
 
4.6%
Y 212
 
4.6%
2 161
 
3.5%
Other values (10) 1192
25.9%

PHQ1
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
251 
0
166 
3
142 
2
99 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

Length

2023-01-12T12:07:52.905922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:53.309134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

Most occurring characters

ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 251
38.1%
0 166
25.2%
3 142
21.6%
2 99
 
15.0%

PHQ2
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
219 
1
207 
3
130 
2
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

Length

2023-01-12T12:07:53.649240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:53.995575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

Most occurring characters

ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 219
33.3%
1 207
31.5%
3 130
19.8%
2 102
15.5%

PHQ3
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
265 
1
163 
3
132 
2
96 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

Length

2023-01-12T12:07:54.323175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:54.646676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 265
40.3%
1 163
24.8%
3 132
20.1%
2 96
 
14.6%
4 2
 
0.3%

PHQ4
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
234 
0
208 
2
126 
3
90 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

Length

2023-01-12T12:07:54.942445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:55.152237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

Most occurring characters

ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 234
35.6%
0 208
31.6%
2 126
19.1%
3 90
 
13.7%

PHQ5
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
326 
1
157 
3
107 
2
68 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

Length

2023-01-12T12:07:55.389802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:55.634350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 326
49.5%
1 157
23.9%
3 107
 
16.3%
2 68
 
10.3%

PHQ6
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
223 
1
194 
3
164 
2
77 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

Length

2023-01-12T12:07:55.879407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:56.226205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 223
33.9%
1 194
29.5%
3 164
24.9%
2 77
 
11.7%

PHQ7
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
231 
3
169 
0
163 
2
95 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

Length

2023-01-12T12:07:56.551179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:56.917524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

Most occurring characters

ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 231
35.1%
3 169
25.7%
0 163
24.8%
2 95
14.4%

PHQ8
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
365 
1
153 
2
75 
3
65 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

Length

2023-01-12T12:07:57.222738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:57.459335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365
55.5%
1 153
23.3%
2 75
 
11.4%
3 65
 
9.9%

GAD1
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
239 
0
236 
2
98 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

Length

2023-01-12T12:07:57.735431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:58.037429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

Most occurring characters

ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 239
36.3%
0 236
35.9%
2 98
14.9%
3 85
 
12.9%

GAD2
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
203 
1
198 
3
156 
2
101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

Length

2023-01-12T12:07:58.373657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:58.661207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

Most occurring characters

ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 203
30.9%
1 198
30.1%
3 156
23.7%
2 101
15.3%

GAD3
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
213 
3
192 
0
149 
2
104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

Length

2023-01-12T12:07:58.955681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:59.292142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

Most occurring characters

ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 213
32.4%
3 192
29.2%
0 149
22.6%
2 104
15.8%

GAD4
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
297 
1
176 
3
111 
2
74 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

Length

2023-01-12T12:07:59.474342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:07:59.675296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 297
45.1%
1 176
26.7%
3 111
 
16.9%
2 74
 
11.2%

GAD5
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
362 
1
141 
2
95 
3
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

Length

2023-01-12T12:07:59.866048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:00.065134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 362
55.0%
1 141
 
21.4%
2 95
 
14.4%
3 60
 
9.1%

GAD6
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
1
237 
0
211 
3
135 
2
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row0
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

Length

2023-01-12T12:08:00.736153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:00.951433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 237
36.0%
0 211
32.1%
3 135
20.5%
2 75
 
11.4%

GAD7
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
0
224 
1
186 
3
145 
2
103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

Length

2023-01-12T12:08:01.171257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:01.393981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

Most occurring characters

ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 224
34.0%
1 186
28.3%
3 145
22.0%
2 103
15.7%

MSSS1
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1200608
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:01.680001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8396592
Coefficient of variation (CV)0.35930416
Kurtosis-0.34207796
Mean5.1200608
Median Absolute Deviation (MAD)1
Skewness-0.89527731
Sum3369
Variance3.3843458
MonotonicityNot monotonic
2023-01-12T12:08:01.963951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 191
29.0%
7 173
26.3%
5 107
16.3%
2 55
 
8.4%
4 51
 
7.8%
3 41
 
6.2%
1 40
 
6.1%
ValueCountFrequency (%)
1 40
 
6.1%
2 55
 
8.4%
3 41
 
6.2%
4 51
 
7.8%
5 107
16.3%
6 191
29.0%
7 173
26.3%
ValueCountFrequency (%)
7 173
26.3%
6 191
29.0%
5 107
16.3%
4 51
 
7.8%
3 41
 
6.2%
2 55
 
8.4%
1 40
 
6.1%

MSSS2
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1671733
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:02.245308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8789918
Coefficient of variation (CV)0.36364018
Kurtosis-0.21731857
Mean5.1671733
Median Absolute Deviation (MAD)1
Skewness-0.99427487
Sum3400
Variance3.5306103
MonotonicityNot monotonic
2023-01-12T12:08:02.507647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 209
31.8%
7 179
27.2%
5 94
14.3%
2 53
 
8.1%
1 48
 
7.3%
4 44
 
6.7%
3 31
 
4.7%
ValueCountFrequency (%)
1 48
 
7.3%
2 53
 
8.1%
3 31
 
4.7%
4 44
 
6.7%
5 94
14.3%
6 209
31.8%
7 179
27.2%
ValueCountFrequency (%)
7 179
27.2%
6 209
31.8%
5 94
14.3%
4 44
 
6.7%
3 31
 
4.7%
2 53
 
8.1%
1 48
 
7.3%

MSSS3
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.843465
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:02.760389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5880437
Coefficient of variation (CV)0.27176404
Kurtosis1.9676509
Mean5.843465
Median Absolute Deviation (MAD)1
Skewness-1.6607222
Sum3845
Variance2.5218826
MonotonicityNot monotonic
2023-01-12T12:08:02.925515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 300
45.6%
6 203
30.9%
5 50
 
7.6%
4 33
 
5.0%
2 25
 
3.8%
3 24
 
3.6%
1 23
 
3.5%
ValueCountFrequency (%)
1 23
 
3.5%
2 25
 
3.8%
3 24
 
3.6%
4 33
 
5.0%
5 50
 
7.6%
6 203
30.9%
7 300
45.6%
ValueCountFrequency (%)
7 300
45.6%
6 203
30.9%
5 50
 
7.6%
4 33
 
5.0%
3 24
 
3.6%
2 25
 
3.8%
1 23
 
3.5%

MSSS4
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3054711
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:03.112634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.818056
Coefficient of variation (CV)0.3426757
Kurtosis0.016692442
Mean5.3054711
Median Absolute Deviation (MAD)1
Skewness-1.0476993
Sum3491
Variance3.3053277
MonotonicityNot monotonic
2023-01-12T12:08:03.294531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 214
32.5%
6 175
26.6%
5 106
16.1%
4 46
 
7.0%
2 44
 
6.7%
1 39
 
5.9%
3 34
 
5.2%
ValueCountFrequency (%)
1 39
 
5.9%
2 44
 
6.7%
3 34
 
5.2%
4 46
 
7.0%
5 106
16.1%
6 175
26.6%
7 214
32.5%
ValueCountFrequency (%)
7 214
32.5%
6 175
26.6%
5 106
16.1%
4 46
 
7.0%
3 34
 
5.2%
2 44
 
6.7%
1 39
 
5.9%

MSSS5
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2887538
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:03.534285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8477584
Coefficient of variation (CV)0.34937501
Kurtosis-0.10669127
Mean5.2887538
Median Absolute Deviation (MAD)1
Skewness-1.0103597
Sum3480
Variance3.4142112
MonotonicityNot monotonic
2023-01-12T12:08:03.756160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 219
33.3%
6 176
26.7%
5 82
 
12.5%
4 65
 
9.9%
2 45
 
6.8%
1 41
 
6.2%
3 30
 
4.6%
ValueCountFrequency (%)
1 41
 
6.2%
2 45
 
6.8%
3 30
 
4.6%
4 65
 
9.9%
5 82
 
12.5%
6 176
26.7%
7 219
33.3%
ValueCountFrequency (%)
7 219
33.3%
6 176
26.7%
5 82
 
12.5%
4 65
 
9.9%
3 30
 
4.6%
2 45
 
6.8%
1 41
 
6.2%

MSSS6
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.556231
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:04.028639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7238675
Coefficient of variation (CV)0.37835384
Kurtosis-0.55351709
Mean4.556231
Median Absolute Deviation (MAD)1
Skewness-0.62739908
Sum2998
Variance2.9717191
MonotonicityNot monotonic
2023-01-12T12:08:04.309723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 174
26.4%
5 159
24.2%
4 100
15.2%
7 61
 
9.3%
3 58
 
8.8%
1 54
 
8.2%
2 52
 
7.9%
ValueCountFrequency (%)
1 54
 
8.2%
2 52
 
7.9%
3 58
 
8.8%
4 100
15.2%
5 159
24.2%
6 174
26.4%
7 61
 
9.3%
ValueCountFrequency (%)
7 61
 
9.3%
6 174
26.4%
5 159
24.2%
4 100
15.2%
3 58
 
8.8%
2 52
 
7.9%
1 54
 
8.2%

MSSS7
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2705167
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:04.575478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8879769
Coefficient of variation (CV)0.44209565
Kurtosis-1.1046754
Mean4.2705167
Median Absolute Deviation (MAD)1
Skewness-0.35153868
Sum2810
Variance3.5644567
MonotonicityNot monotonic
2023-01-12T12:08:04.848485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 146
22.2%
5 142
21.6%
2 95
14.4%
4 87
13.2%
1 71
10.8%
7 66
10.0%
3 51
 
7.8%
ValueCountFrequency (%)
1 71
10.8%
2 95
14.4%
3 51
 
7.8%
4 87
13.2%
5 142
21.6%
6 146
22.2%
7 66
10.0%
ValueCountFrequency (%)
7 66
10.0%
6 146
22.2%
5 142
21.6%
4 87
13.2%
3 51
 
7.8%
2 95
14.4%
1 71
10.8%

MSSS8
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8799392
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:05.142245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9760804
Coefficient of variation (CV)0.40493955
Kurtosis-0.74684201
Mean4.8799392
Median Absolute Deviation (MAD)1
Skewness-0.7157945
Sum3211
Variance3.9048938
MonotonicityNot monotonic
2023-01-12T12:08:05.422407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 164
24.9%
6 161
24.5%
5 110
16.7%
4 65
 
9.9%
1 63
 
9.6%
2 61
 
9.3%
3 34
 
5.2%
ValueCountFrequency (%)
1 63
 
9.6%
2 61
 
9.3%
3 34
 
5.2%
4 65
 
9.9%
5 110
16.7%
6 161
24.5%
7 164
24.9%
ValueCountFrequency (%)
7 164
24.9%
6 161
24.5%
5 110
16.7%
4 65
 
9.9%
3 34
 
5.2%
2 61
 
9.3%
1 63
 
9.6%

MSSS9
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8541033
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:05.615067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8056203
Coefficient of variation (CV)0.37197814
Kurtosis-0.46634657
Mean4.8541033
Median Absolute Deviation (MAD)1
Skewness-0.77243936
Sum3194
Variance3.2602647
MonotonicityNot monotonic
2023-01-12T12:08:05.855742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 194
29.5%
5 138
21.0%
7 112
17.0%
4 65
 
9.9%
2 53
 
8.1%
1 49
 
7.4%
3 47
 
7.1%
ValueCountFrequency (%)
1 49
 
7.4%
2 53
 
8.1%
3 47
 
7.1%
4 65
 
9.9%
5 138
21.0%
6 194
29.5%
7 112
17.0%
ValueCountFrequency (%)
7 112
17.0%
6 194
29.5%
5 138
21.0%
4 65
 
9.9%
3 47
 
7.1%
2 53
 
8.1%
1 49
 
7.4%

MSSS10
Real number (ℝ)

Distinct8
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1534954
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:06.151212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9737613
Coefficient of variation (CV)0.38299467
Kurtosis-0.60108279
Mean5.1534954
Median Absolute Deviation (MAD)1
Skewness-0.83867945
Sum3391
Variance3.8957336
MonotonicityNot monotonic
2023-01-12T12:08:06.450279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 227
34.5%
6 147
22.3%
5 81
 
12.3%
2 59
 
9.0%
4 56
 
8.5%
1 48
 
7.3%
3 39
 
5.9%
8 1
 
0.2%
ValueCountFrequency (%)
1 48
 
7.3%
2 59
 
9.0%
3 39
 
5.9%
4 56
 
8.5%
5 81
 
12.3%
6 147
22.3%
7 227
34.5%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
7 227
34.5%
6 147
22.3%
5 81
 
12.3%
4 56
 
8.5%
3 39
 
5.9%
2 59
 
9.0%
1 48
 
7.3%

MSSS11
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.449848
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:06.752792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7311037
Coefficient of variation (CV)0.31764256
Kurtosis0.37570406
Mean5.449848
Median Absolute Deviation (MAD)1
Skewness-1.1592233
Sum3586
Variance2.9967199
MonotonicityNot monotonic
2023-01-12T12:08:07.040413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 228
34.7%
6 192
29.2%
5 82
 
12.5%
4 56
 
8.5%
3 35
 
5.3%
2 34
 
5.2%
1 31
 
4.7%
ValueCountFrequency (%)
1 31
 
4.7%
2 34
 
5.2%
3 35
 
5.3%
4 56
 
8.5%
5 82
 
12.5%
6 192
29.2%
7 228
34.7%
ValueCountFrequency (%)
7 228
34.7%
6 192
29.2%
5 82
 
12.5%
4 56
 
8.5%
3 35
 
5.3%
2 34
 
5.2%
1 31
 
4.7%

MSSS12
Real number (ℝ)

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2066869
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:07.326962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8693598
Coefficient of variation (CV)0.44437817
Kurtosis-1.0725747
Mean4.2066869
Median Absolute Deviation (MAD)1
Skewness-0.34434921
Sum2768
Variance3.4945062
MonotonicityNot monotonic
2023-01-12T12:08:07.604080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 155
23.6%
6 136
20.7%
4 84
12.8%
2 82
12.5%
1 79
12.0%
3 64
9.7%
7 58
 
8.8%
ValueCountFrequency (%)
1 79
12.0%
2 82
12.5%
3 64
9.7%
4 84
12.8%
5 155
23.6%
6 136
20.7%
7 58
 
8.8%
ValueCountFrequency (%)
7 58
 
8.8%
6 136
20.7%
5 155
23.6%
4 84
12.8%
3 64
9.7%
2 82
12.5%
1 79
12.0%

Tribe
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Minority
431 
Majority
227 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5264
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinority
2nd rowMinority
3rd rowMinority
4th rowMinority
5th rowMinority

Common Values

ValueCountFrequency (%)
Minority 431
65.5%
Majority 227
34.5%

Length

2023-01-12T12:08:07.958337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:08.200937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
minority 431
65.5%
majority 227
34.5%

Most occurring characters

ValueCountFrequency (%)
i 1089
20.7%
M 658
12.5%
o 658
12.5%
r 658
12.5%
t 658
12.5%
y 658
12.5%
n 431
 
8.2%
a 227
 
4.3%
j 227
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4606
87.5%
Uppercase Letter 658
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1089
23.6%
o 658
14.3%
r 658
14.3%
t 658
14.3%
y 658
14.3%
n 431
 
9.4%
a 227
 
4.9%
j 227
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
M 658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5264
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1089
20.7%
M 658
12.5%
o 658
12.5%
r 658
12.5%
t 658
12.5%
y 658
12.5%
n 431
 
8.2%
a 227
 
4.3%
j 227
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1089
20.7%
M 658
12.5%
o 658
12.5%
r 658
12.5%
t 658
12.5%
y 658
12.5%
n 431
 
8.2%
a 227
 
4.3%
j 227
 
4.3%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
F
338 
M
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters658
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowF
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 338
51.4%
M 320
48.6%

Length

2023-01-12T12:08:08.404104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:08.603286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
f 338
51.4%
m 320
48.6%

Most occurring characters

ValueCountFrequency (%)
F 338
51.4%
M 320
48.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 658
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 338
51.4%
M 320
48.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 658
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 338
51.4%
M 320
48.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 338
51.4%
M 320
48.6%

School
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Olympic
212 
AGHS
157 
AHS
122 
Elite
87 
Starays
80 

Length

Max length7
Median length5
Mean length5.2781155
Min length3

Characters and Unicode

Total characters3473
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStarays
2nd rowStarays
3rd rowStarays
4th rowStarays
5th rowStarays

Common Values

ValueCountFrequency (%)
Olympic 212
32.2%
AGHS 157
23.9%
AHS 122
18.5%
Elite 87
13.2%
Starays 80
 
12.2%

Length

2023-01-12T12:08:08.777993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:09.052224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
olympic 212
32.2%
aghs 157
23.9%
ahs 122
18.5%
elite 87
13.2%
starays 80
 
12.2%

Most occurring characters

ValueCountFrequency (%)
S 359
10.3%
i 299
 
8.6%
l 299
 
8.6%
y 292
 
8.4%
A 279
 
8.0%
H 279
 
8.0%
O 212
 
6.1%
m 212
 
6.1%
p 212
 
6.1%
c 212
 
6.1%
Other values (7) 818
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2100
60.5%
Uppercase Letter 1373
39.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 299
14.2%
l 299
14.2%
y 292
13.9%
m 212
10.1%
p 212
10.1%
c 212
10.1%
t 167
8.0%
a 160
7.6%
e 87
 
4.1%
r 80
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
S 359
26.1%
A 279
20.3%
H 279
20.3%
O 212
15.4%
G 157
11.4%
E 87
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3473
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 359
10.3%
i 299
 
8.6%
l 299
 
8.6%
y 292
 
8.4%
A 279
 
8.0%
H 279
 
8.0%
O 212
 
6.1%
m 212
 
6.1%
p 212
 
6.1%
c 212
 
6.1%
Other values (7) 818
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 359
10.3%
i 299
 
8.6%
l 299
 
8.6%
y 292
 
8.4%
A 279
 
8.0%
H 279
 
8.0%
O 212
 
6.1%
m 212
 
6.1%
p 212
 
6.1%
c 212
 
6.1%
Other values (7) 818
23.6%

Age
Real number (ℝ)

Distinct14
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.847264
Minimum12
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2023-01-12T12:08:09.392965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile14
Q115
median16
Q317
95-th percentile18
Maximum25
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4130831
Coefficient of variation (CV)0.089168896
Kurtosis3.8699815
Mean15.847264
Median Absolute Deviation (MAD)1
Skewness1.0234834
Sum10427.5
Variance1.9968038
MonotonicityNot monotonic
2023-01-12T12:08:09.581568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
16 199
30.2%
15 179
27.2%
17 117
17.8%
14 84
12.8%
18 42
 
6.4%
19 15
 
2.3%
13 13
 
2.0%
20 3
 
0.5%
21 1
 
0.2%
22 1
 
0.2%
Other values (4) 4
 
0.6%
ValueCountFrequency (%)
12 1
 
0.2%
13 13
 
2.0%
14 84
12.8%
15 179
27.2%
16 199
30.2%
17 117
17.8%
18 42
 
6.4%
19 15
 
2.3%
20 3
 
0.5%
20.5 1
 
0.2%
ValueCountFrequency (%)
25 1
 
0.2%
23 1
 
0.2%
22 1
 
0.2%
21 1
 
0.2%
20.5 1
 
0.2%
20 3
 
0.5%
19 15
 
2.3%
18 42
 
6.4%
17 117
17.8%
16 199
30.2%

School_Resources
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Rich
279 
Medium
212 
Poor
167 

Length

Max length6
Median length4
Mean length4.6443769
Min length4

Characters and Unicode

Total characters3056
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoor
2nd rowPoor
3rd rowPoor
4th rowPoor
5th rowPoor

Common Values

ValueCountFrequency (%)
Rich 279
42.4%
Medium 212
32.2%
Poor 167
25.4%

Length

2023-01-12T12:08:09.844941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T12:08:10.080640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rich 279
42.4%
medium 212
32.2%
poor 167
25.4%

Most occurring characters

ValueCountFrequency (%)
i 491
16.1%
o 334
10.9%
R 279
9.1%
c 279
9.1%
h 279
9.1%
M 212
6.9%
e 212
6.9%
d 212
6.9%
u 212
6.9%
m 212
6.9%
Other values (2) 334
10.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2398
78.5%
Uppercase Letter 658
 
21.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 491
20.5%
o 334
13.9%
c 279
11.6%
h 279
11.6%
e 212
8.8%
d 212
8.8%
u 212
8.8%
m 212
8.8%
r 167
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
R 279
42.4%
M 212
32.2%
P 167
25.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3056
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 491
16.1%
o 334
10.9%
R 279
9.1%
c 279
9.1%
h 279
9.1%
M 212
6.9%
e 212
6.9%
d 212
6.9%
u 212
6.9%
m 212
6.9%
Other values (2) 334
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 491
16.1%
o 334
10.9%
R 279
9.1%
c 279
9.1%
h 279
9.1%
M 212
6.9%
e 212
6.9%
d 212
6.9%
u 212
6.9%
m 212
6.9%
Other values (2) 334
10.9%

Interactions

2023-01-12T12:07:46.660064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:06:59.462194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:03.017830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:06.894770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:10.962998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:14.761168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:19.027629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:22.614120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:25.892642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:29.641243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:34.343252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:38.561702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:42.993059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:46.952369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:06:59.946649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:03.325405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:07.170892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:11.264764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:15.030427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:19.321984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:22.899146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:26.182889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:29.969588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:34.735489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:38.868861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:43.222738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:47.231517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:00.243234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:03.626076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:07.717661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:11.577820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:15.287818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:19.616438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:23.188597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:26.429373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:30.381327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:35.108226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:39.678487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:43.513070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:47.557209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:00.511307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:03.911714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:07.983916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:11.903043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:15.576774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:19.840027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:23.462706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:26.726573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:30.689383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:35.389058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:40.002885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:43.778381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:47.853482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:00.793175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:04.223399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:08.269181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:12.210640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:15.857280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:20.096941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:23.670516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:27.032442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:31.004692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:35.624537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:40.298108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:44.052247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:48.124568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:01.049720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:04.509657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:08.546385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:12.511178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:16.180118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:20.307898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:23.880420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:27.311376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:31.365177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:35.963470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:40.596433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:44.306960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:48.419360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:01.315108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:04.800857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:08.834152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:12.765757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:16.570564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:20.524555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:24.097486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:27.598090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:31.695150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:36.305689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:40.943028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:44.590237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:48.714303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:01.531414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:05.117357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:09.101216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:13.017611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:17.016660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:20.731539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:24.308855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:27.831830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:31.993501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:36.647914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:41.264876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:44.877555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:48.972461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:01.742773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:05.386750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:09.409487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:13.298274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:17.411165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:20.995156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:24.559841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:28.133322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:32.295153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:36.983290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:41.556452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:45.159002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:49.240820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:01.966582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:05.692213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:09.682478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:13.593985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:17.674759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:21.280432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:24.777555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:28.429187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:32.719868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:37.310517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:41.799624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:45.513142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:49.558113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:02.199529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:05.981581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:09.984837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:13.843940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:18.036472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:21.825241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:25.043593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:28.714141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:33.119411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:37.630478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:42.105151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:45.852342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:49.865325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:02.437657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:06.299054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:10.313539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:14.163275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:18.434733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:22.046293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:25.339384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:29.026817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:33.458101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:37.929231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:42.407214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:46.124817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:50.152410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:02.712713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:06.600857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:10.649134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:14.445229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:18.721845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:22.326072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:25.615111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:29.314168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:33.865681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:38.173339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:42.704322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-12T12:07:46.362789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-12T12:08:10.325751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
MSSS1MSSS2MSSS3MSSS4MSSS5MSSS6MSSS7MSSS8MSSS9MSSS10MSSS11MSSS12AgePHQ1PHQ2PHQ3PHQ4PHQ5PHQ6PHQ7PHQ8GAD1GAD2GAD3GAD4GAD5GAD6GAD7TribeGenderSchoolSchool_Resources
MSSS11.0000.5770.3070.2890.4810.2860.2660.2510.3480.4050.2160.179-0.1040.0360.0710.0000.0000.0770.0990.0530.0260.0000.0000.0580.0950.0600.0510.0590.0000.1020.0850.069
MSSS20.5771.0000.2630.3280.5310.3020.2780.3060.4720.4800.2860.301-0.1400.0720.1010.0500.0670.1170.0830.0000.0950.0490.0390.0940.0100.0520.0000.0400.0000.0000.0910.116
MSSS30.3070.2631.0000.5520.3530.1680.1320.4350.2030.3160.5350.109-0.1740.0000.1120.0950.0770.0690.1020.0580.0760.0540.1020.0700.0970.0860.0000.0000.0000.0000.1240.172
MSSS40.2890.3280.5521.0000.4050.2250.1870.5220.2870.3060.5010.208-0.2170.0550.1010.0860.0480.1370.1090.0660.0910.1020.0960.0730.0680.0910.0570.0460.0000.0770.1010.135
MSSS50.4810.5310.3530.4051.0000.3170.3140.3150.3990.5630.3790.254-0.1710.0870.1080.0260.0870.0770.1130.0680.0730.0310.0000.1110.0740.0870.0010.0940.0610.0820.1530.182
MSSS60.2860.3020.1680.2250.3171.0000.5300.1850.5340.2840.1840.491-0.0400.0000.0560.0840.0900.0770.0650.0190.0000.0640.0820.1300.0680.1040.0930.0540.0450.0600.1100.125
MSSS70.2660.2780.1320.1870.3140.5301.0000.1420.4840.2780.1930.428-0.0270.0630.0000.0180.0150.0510.0520.0750.0000.0000.0460.0860.0000.0720.0990.0000.0800.0760.1490.189
MSSS80.2510.3060.4350.5220.3150.1850.1421.0000.2910.2980.4750.224-0.1220.0850.1240.1270.0960.1010.0870.1110.0310.0670.0880.0780.0790.1340.0550.0000.0000.0220.0860.079
MSSS90.3480.4720.2030.2870.3990.5340.4840.2911.0000.3870.2610.5530.0020.0480.0000.0360.1010.0490.1020.0530.0000.0000.0810.0870.0940.0730.0000.0000.0000.0000.0840.095
MSSS100.4050.4800.3160.3060.5630.2840.2780.2980.3871.0000.3870.258-0.1460.0560.1120.0610.0000.0000.0620.0000.0180.0300.0650.0670.0000.1230.0000.0710.0440.0000.1240.175
MSSS110.2160.2860.5350.5010.3790.1840.1930.4750.2610.3871.0000.173-0.1720.0840.1100.0560.0720.0360.0900.0930.1150.0910.1170.0700.0600.0660.0410.0000.0790.0250.1280.169
MSSS120.1790.3010.1090.2080.2540.4910.4280.2240.5530.2580.1731.000-0.0160.0000.0650.0180.0590.0000.0000.0620.0390.0740.0840.0970.0000.0000.0260.0000.1050.0370.1290.167
Age-0.104-0.140-0.174-0.217-0.171-0.040-0.027-0.1220.002-0.146-0.172-0.0161.0000.0390.0670.0000.0810.0430.0690.0730.0790.0850.0940.0800.0610.1220.0080.0290.1750.1050.2450.306
PHQ10.0360.0720.0000.0550.0870.0000.0630.0850.0480.0560.0840.0000.0391.0000.1350.0900.1170.1670.1480.2260.1590.2150.1470.1570.1760.1150.1550.1350.0000.0000.0470.053
PHQ20.0710.1010.1120.1010.1080.0560.0000.1240.0000.1120.1100.0650.0670.1351.0000.1260.2040.1250.2940.1890.1690.2540.2830.2940.1760.1840.1810.2100.0910.1390.1030.091
PHQ30.0000.0500.0950.0860.0260.0840.0180.1270.0360.0610.0560.0180.0000.0900.1261.0000.1770.1730.1720.1710.1080.1380.1440.1310.1530.1050.1600.1040.0510.0390.0720.118
PHQ40.0000.0670.0770.0480.0870.0900.0150.0960.1010.0000.0720.0590.0810.1170.2040.1771.0000.1500.2060.1570.1700.2360.1660.2180.1910.1670.1250.1880.0000.0890.0540.034
PHQ50.0770.1170.0690.1370.0770.0770.0510.1010.0490.0000.0360.0000.0430.1670.1250.1730.1501.0000.1510.1230.1810.2350.1770.1620.1670.1590.1870.1730.0000.0000.0470.053
PHQ60.0990.0830.1020.1090.1130.0650.0520.0870.1020.0620.0900.0000.0690.1480.2940.1720.2060.1511.0000.2050.1700.2480.2420.2630.1850.1840.1530.2100.0000.0610.0000.000
PHQ70.0530.0000.0580.0660.0680.0190.0750.1110.0530.0000.0930.0620.0730.2260.1890.1710.1570.1230.2051.0000.1340.1880.2400.2270.1520.1790.1390.1980.0000.0000.0300.059
PHQ80.0260.0950.0760.0910.0730.0000.0000.0310.0000.0180.1150.0390.0790.1590.1690.1080.1700.1810.1700.1341.0000.2210.1530.1870.1850.2110.1760.1370.0550.0580.0620.075
GAD10.0000.0490.0540.1020.0310.0640.0000.0670.0000.0300.0910.0740.0850.2150.2540.1380.2360.2350.2480.1880.2211.0000.2480.2510.2250.2740.1860.2610.0840.0390.0880.112
GAD20.0000.0390.1020.0960.0000.0820.0460.0880.0810.0650.1170.0840.0940.1470.2830.1440.1660.1770.2420.2400.1530.2481.0000.3320.2320.1870.1770.2520.0700.1230.0400.000
GAD30.0580.0940.0700.0730.1110.1300.0860.0780.0870.0670.0700.0970.0800.1570.2940.1310.2180.1620.2630.2270.1870.2510.3321.0000.2040.1620.1900.2140.1260.0610.0710.091
GAD40.0950.0100.0970.0680.0740.0680.0000.0790.0940.0000.0600.0000.0610.1760.1760.1530.1910.1670.1850.1520.1850.2250.2320.2041.0000.2510.1370.1560.0000.0500.0000.000
GAD50.0600.0520.0860.0910.0870.1040.0720.1340.0730.1230.0660.0000.1220.1150.1840.1050.1670.1590.1840.1790.2110.2740.1870.1620.2511.0000.1760.2010.1290.0650.0860.084
GAD60.0510.0000.0000.0570.0010.0930.0990.0550.0000.0000.0410.0260.0080.1550.1810.1600.1250.1870.1530.1390.1760.1860.1770.1900.1370.1761.0000.1790.0000.0100.0290.042
GAD70.0590.0400.0000.0460.0940.0540.0000.0000.0000.0710.0000.0000.0290.1350.2100.1040.1880.1730.2100.1980.1370.2610.2520.2140.1560.2010.1791.0000.0490.0000.0410.044
Tribe0.0000.0000.0000.0000.0610.0450.0800.0000.0000.0440.0790.1050.1750.0000.0910.0510.0000.0000.0000.0000.0550.0840.0700.1260.0000.1290.0000.0491.0000.0500.5300.526
Gender0.1020.0000.0000.0770.0820.0600.0760.0220.0000.0000.0250.0370.1050.0000.1390.0390.0890.0000.0610.0000.0580.0390.1230.0610.0500.0650.0100.0000.0501.0000.6570.121
School0.0850.0910.1240.1010.1530.1100.1490.0860.0840.1240.1280.1290.2450.0470.1030.0720.0540.0470.0000.0300.0620.0880.0400.0710.0000.0860.0290.0410.5300.6571.0000.998
School_Resources0.0690.1160.1720.1350.1820.1250.1890.0790.0950.1750.1690.1670.3060.0530.0910.1180.0340.0530.0000.0590.0750.1120.0000.0910.0000.0840.0420.0440.5260.1210.9981.000

Missing values

2023-01-12T12:07:50.625228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-12T12:07:51.912951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ParticipantIDPHQ1PHQ2PHQ3PHQ4PHQ5PHQ6PHQ7PHQ8GAD1GAD2GAD3GAD4GAD5GAD6GAD7MSSS1MSSS2MSSS3MSSS4MSSS5MSSS6MSSS7MSSS8MSSS9MSSS10MSSS11MSSS12TribeGenderSchoolAgeSchool_Resources
0SR_001302111311233130116515676766MinorityMStarays18.0Poor
1SR_002301001220001100146553275736MinorityMStarays16.0Poor
2SR_003230123120231031227525252255MinorityFStarays14.0Poor
3SR_004131121313331132445454344543MinorityMStarays20.0Poor
4SR_005110103301331010666662266262MinorityMStarays18.0Poor
5SR_006020200202020202767672476567MinorityFStarays16.0Poor
6SR_007001100130031031776755566762MinorityFStarays16.0Poor
7SR_008200230203212302466654516265MinorityMStarays18.0Poor
8SR_009100000203000102666262464242MinorityMStarays17.0Poor
9SR_010120301301321031567552573264MinorityMStarays15.0Poor
ParticipantIDPHQ1PHQ2PHQ3PHQ4PHQ5PHQ6PHQ7PHQ8GAD1GAD2GAD3GAD4GAD5GAD6GAD7MSSS1MSSS2MSSS3MSSS4MSSS5MSSS6MSSS7MSSS8MSSS9MSSS10MSSS11MSSS12TribeGenderSchoolAgeSchool_Resources
648OLY_203332133322202013715571151161MinorityFOlympic17.0Medium
649OLY_204110202301110111767766676676MinorityFOlympic17.0Medium
650OLY_205023102310130010567765376266MinorityFOlympic16.0Medium
651OLY_206110101000100211776665565523MinorityFOlympic17.0Medium
652OLY_207212001000010012657555565565MinorityFOlympic16.0Medium
653OLY_208321102301123131767776465475MinorityFOlympic15.0Medium
654OLY_209333333330233322426444443345MajorityFOlympic16.0Medium
655OLY_210233323322321333777676656776MinorityFOlympic16.0Medium
656OLY_211000000000100011556255565255MajorityFOlympic16.0Medium
657OLY_212021101120112212555553555655MinorityFOlympic16.0Medium